Lane Detection Algorithm Using LRF for Autonomous Navigation of Mobile Robot

Abstract

This paper proposes a lane detection algorithm using a laser range finder (LRF) for the autonomous navigation of a mobile robot. There are many technologies for ensuring the safety of vehicles, such as airbags, ABS, and EPS. Further, lane detection is a fundamental requirement for an automobile system that utilizes the external environment information of automobiles. Representative methods of lane recognition are vision-based and LRF-based systems. In the case of a vision-based system, the recognition of the environment of a three-dimensional space becomes excellent only in good conditions for capturing images. However, there are so many unexpected barriers, such as bad illumination, occlusions, vibrations, and thick fog, that the vision-based method cannot be used for satisfying the abovementioned fundamental requirement. In this paper, a three-dimensional lane detection algorithm using LRF that is very robust against illumination is proposed. For the three-dimensional lane detection, the laser reflection difference between the asphalt and the lane according to color and distance has been utilized with the extraction of feature points. Further, a stable tracking algorithm is introduced empirically in this research. The performance of the proposed algorithm of lane detection and tracking has been experimentally verified.

Curve-Aware Model Predictive Control (C-MPC) Trajectory Tracking for Automated Guided Vehicle (AGV) over On-Road, In-Door, and Agricultural-Land

“…According to J.H. Han et al [19], a web camera installed on the AGV was used to extract road features, and the AGV successfully traveled, as shown in Figure 21a,b. The test is conducted on a bright and sunny day.…”

“…J.H. Han et al [19] used a feature point extraction technique with LRF calibration and amplification errors dependent on the materials and colors of the asphalt and lanes to create a laser-scanned 3D road map and to identify and recognize lanes. The test results revealed that using their method could assure safe driving under poor road conditions, such as fog and curvature paths, which might help in the research and development of autonomous driving technologies.…”